Hybrid-driven MRF seismic inversion for gas sand identification: A case study in the Yinggehai Basin

Abstract

The Yinggehai Basin displays anomalies characterized by heightened levels of temperature and pressure. It represents a depositional model of a submarine fan with gravity-driven flow, demonstrating significant lateral reservoir heterogeneity and intricate spatial distribution of the gas reservoir. Identifying gas formations using elastic parameters as indicators relies on stable seismic inversion results. This requires regularization to alleviate ill-posedness in the inverse problem and to construct model features of the subsurface medium. Markov Random Field (MRF)is an effective soft-constrained regularization method. It enhances the marginal features of inversion results by penalizing the objective function with the gradient of neighborhood points. However, the standard MRF method relies only on the parameter model-driven and has poor applicability in areas with strong lateral inhomogeneity or complex depositional processes. In this research paper, we propose a novel approach to seismic inversion that integrates MRF neighborhood drive and incorporates both seismic data and a parametric model. Multi-order MRF neighborhoods are constructed by using seismic data in the horizontal direction (including horizontal diagonal) and parametric model data in the vertical direction (including vertical diagonal). The model-driven results are also utilized to couple seismic data and improve the stability of the hybrid-driven MRF inversion. In addition, we select the P-impedance as the parameter for inversion due to its heightened sensitivity towards gas formation within the study region. Consequently, we utilize the inversion results to delineate the presence of sandstone in the target layer and discern any indications of gas formation. The implementation of this method in the field has demonstrated its capability to enhance the stability of inversion outcomes, effectively integrating the lateral consistency of seismic data with the vertical precision of parametric model data. This approach significantly improves reservoir heterogeneity characterization and enhances accuracy in identifying sandstone and gas.